Overview

Dataset statistics

Number of variables10
Number of observations413
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.4 KiB
Average record size in memory85.3 B

Variable types

Categorical6
Boolean1
Numeric3

Dataset

Description대전광역시 중구 지방세의 연도별 납부 매체(가상계좌, ARS, 위택스 등)를 확인할 수 있으며, 이 납부 매체가 전자고지인가의 여부와 납부 건수·금액 등을 살펴볼 수 있습니다.
URLhttps://www.data.go.kr/data/15078565/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
납부매체전자고지여부 is highly overall correlated with 납부매체High correlation
납부매체 is highly overall correlated with 납부매체전자고지여부High correlation
납부건수 is highly overall correlated with 납부금액 and 1 other fieldsHigh correlation
납부금액 is highly overall correlated with 납부건수 and 1 other fieldsHigh correlation
납부매체비율 is highly overall correlated with 납부건수 and 1 other fieldsHigh correlation
납부매체비율 has 26 (6.3%) zerosZeros

Reproduction

Analysis started2023-12-12 03:14:56.460707
Analysis finished2023-12-12 03:14:58.620866
Duration2.16 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
대전광역시
413 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대전광역시
2nd row대전광역시
3rd row대전광역시
4th row대전광역시
5th row대전광역시

Common Values

ValueCountFrequency (%)
대전광역시 413
100.0%

Length

2023-12-12T12:14:58.716146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:14:58.854321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 413
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
중구
413 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row중구
2nd row중구
3rd row중구
4th row중구
5th row중구

Common Values

ValueCountFrequency (%)
중구 413
100.0%

Length

2023-12-12T12:14:59.000124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:14:59.144151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중구 413
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
30140
413 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30140
2nd row30140
3rd row30140
4th row30140
5th row30140

Common Values

ValueCountFrequency (%)
30140 413
100.0%

Length

2023-12-12T12:14:59.295126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:14:59.443443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30140 413
100.0%

납부연도
Categorical

Distinct5
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2017
84 
2019
84 
2020
83 
2021
82 
2018
80 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2017 84
20.3%
2019 84
20.3%
2020 83
20.1%
2021 82
19.9%
2018 80
19.4%

Length

2023-12-12T12:14:59.589241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:14:59.785051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 84
20.3%
2019 84
20.3%
2020 83
20.1%
2021 82
19.9%
2018 80
19.4%

세목명
Categorical

Distinct13
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
자동차세
53 
주민세
53 
등록면허세
52 
재산세
52 
지방소득세
45 
Other values (8)
158 

Length

Max length7
Median length5
Mean length4.0605327
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row등록면허세
2nd row등록면허세
3rd row자동차세
4th row자동차세
5th row재산세

Common Values

ValueCountFrequency (%)
자동차세 53
12.8%
주민세 53
12.8%
등록면허세 52
12.6%
재산세 52
12.6%
지방소득세 45
10.9%
취득세 40
9.7%
지역자원시설세 35
8.5%
등록세 31
7.5%
면허세 23
5.6%
종합토지세 18
 
4.4%
Other values (3) 11
 
2.7%

Length

2023-12-12T12:14:59.962099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
자동차세 53
12.8%
주민세 53
12.8%
등록면허세 52
12.6%
재산세 52
12.6%
지방소득세 45
10.9%
취득세 40
9.7%
지역자원시설세 35
8.5%
등록세 31
7.5%
면허세 23
5.6%
종합토지세 18
 
4.4%
Other values (3) 11
 
2.7%

납부매체
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
가상계좌
56 
ARS
51 
지자체방문
49 
기타
48 
은행창구
48 
Other values (5)
161 

Length

Max length5
Median length4
Mean length3.9007264
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowARS
2nd rowARS
3rd rowARS
4th rowARS
5th rowARS

Common Values

ValueCountFrequency (%)
가상계좌 56
13.6%
ARS 51
12.3%
지자체방문 49
11.9%
기타 48
11.6%
은행창구 48
11.6%
자동화기기 45
10.9%
위택스 42
10.2%
인터넷지로 39
9.4%
자동이체 20
 
4.8%
페이사납부 15
 
3.6%

Length

2023-12-12T12:15:00.172485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:15:00.366297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가상계좌 56
13.6%
ars 51
12.3%
지자체방문 49
11.9%
기타 48
11.6%
은행창구 48
11.6%
자동화기기 45
10.9%
위택스 42
10.2%
인터넷지로 39
9.4%
자동이체 20
 
4.8%
페이사납부 15
 
3.6%

납부매체전자고지여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size545.0 B
False
220 
True
193 
ValueCountFrequency (%)
False 220
53.3%
True 193
46.7%
2023-12-12T12:15:00.565552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납부건수
Real number (ℝ)

HIGH CORRELATION 

Distinct307
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6960.1525
Minimum1
Maximum91164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-12-12T12:15:00.752248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q117
median810
Q35973
95-th percentile35591.2
Maximum91164
Range91163
Interquartile range (IQR)5956

Descriptive statistics

Standard deviation14516.846
Coefficient of variation (CV)2.085708
Kurtosis12.119437
Mean6960.1525
Median Absolute Deviation (MAD)808
Skewness3.3188782
Sum2874543
Variance2.1073881 × 108
MonotonicityNot monotonic
2023-12-12T12:15:00.961432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 20
 
4.8%
2 16
 
3.9%
4 10
 
2.4%
10 9
 
2.2%
3 8
 
1.9%
14 6
 
1.5%
27 6
 
1.5%
7 5
 
1.2%
6 5
 
1.2%
9 4
 
1.0%
Other values (297) 324
78.5%
ValueCountFrequency (%)
1 20
4.8%
2 16
3.9%
3 8
 
1.9%
4 10
2.4%
5 3
 
0.7%
6 5
 
1.2%
7 5
 
1.2%
8 4
 
1.0%
9 4
 
1.0%
10 9
2.2%
ValueCountFrequency (%)
91164 1
0.2%
85157 1
0.2%
82045 1
0.2%
80174 1
0.2%
74689 1
0.2%
73871 1
0.2%
72963 1
0.2%
67950 1
0.2%
61721 1
0.2%
57327 1
0.2%

납부금액
Real number (ℝ)

HIGH CORRELATION 

Distinct412
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1016095 × 109
Minimum1730
Maximum6.1916854 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-12-12T12:15:01.164968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1730
5-th percentile51400
Q11802980
median1.260634 × 108
Q31.5791115 × 109
95-th percentile1.0496951 × 1010
Maximum6.1916854 × 1010
Range6.1916852 × 1010
Interquartile range (IQR)1.5773085 × 109

Descriptive statistics

Standard deviation5.3147917 × 109
Coefficient of variation (CV)2.528915
Kurtosis55.239768
Mean2.1016095 × 109
Median Absolute Deviation (MAD)1.2602632 × 108
Skewness6.2461045
Sum8.6796471 × 1011
Variance2.8247011 × 1019
MonotonicityNot monotonic
2023-12-12T12:15:01.393611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27810 2
 
0.5%
130104590 1
 
0.2%
1822689480 1
 
0.2%
5650253980 1
 
0.2%
3044886720 1
 
0.2%
28658590 1
 
0.2%
3842891260 1
 
0.2%
543250 1
 
0.2%
67370 1
 
0.2%
7202000000 1
 
0.2%
Other values (402) 402
97.3%
ValueCountFrequency (%)
1730 1
0.2%
5590 1
0.2%
8230 1
0.2%
11600 1
0.2%
14210 1
0.2%
17210 1
0.2%
18240 1
0.2%
18540 1
0.2%
18900 1
0.2%
19000 1
0.2%
ValueCountFrequency (%)
61916854010 1
0.2%
48160286150 1
0.2%
32881083490 1
0.2%
23632631420 1
0.2%
22466623230 1
0.2%
17276870790 1
0.2%
15982092860 1
0.2%
14135187340 1
0.2%
14106654020 1
0.2%
13578159950 1
0.2%

납부매체비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct276
Distinct (%)66.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.622349
Minimum0
Maximum81.8
Zeros26
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-12-12T12:15:01.629930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.09
median3.78
Q319.76
95-th percentile40.426
Maximum81.8
Range81.8
Interquartile range (IQR)19.67

Descriptive statistics

Standard deviation15.490473
Coefficient of variation (CV)1.3328178
Kurtosis3.2164012
Mean11.622349
Median Absolute Deviation (MAD)3.77
Skewness1.7114366
Sum4800.03
Variance239.95475
MonotonicityNot monotonic
2023-12-12T12:15:01.868372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 26
 
6.3%
0.01 19
 
4.6%
0.02 13
 
3.1%
0.12 10
 
2.4%
0.09 9
 
2.2%
0.11 9
 
2.2%
0.06 8
 
1.9%
0.04 8
 
1.9%
0.08 7
 
1.7%
0.05 7
 
1.7%
Other values (266) 297
71.9%
ValueCountFrequency (%)
0.0 26
6.3%
0.01 19
4.6%
0.02 13
3.1%
0.03 5
 
1.2%
0.04 8
 
1.9%
0.05 7
 
1.7%
0.06 8
 
1.9%
0.07 4
 
1.0%
0.08 7
 
1.7%
0.09 9
 
2.2%
ValueCountFrequency (%)
81.8 1
0.2%
80.37 1
0.2%
76.8 1
0.2%
70.09 1
0.2%
68.15 1
0.2%
61.66 1
0.2%
61.56 1
0.2%
55.34 1
0.2%
55.16 1
0.2%
55.1 1
0.2%

Interactions

2023-12-12T12:14:57.823088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:14:57.005949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:14:57.392757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:14:57.945256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:14:57.123682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:14:57.522644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:14:58.093794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:14:57.258072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:14:57.663649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:15:02.031744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부연도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
납부연도1.0000.0000.0000.0000.0000.0350.000
세목명0.0001.0000.1310.0300.2660.3670.603
납부매체0.0000.1311.0000.9950.5460.2700.639
납부매체전자고지여부0.0000.0300.9951.0000.2080.1420.145
납부건수0.0000.2660.5460.2081.0000.5590.764
납부금액0.0350.3670.2700.1420.5591.0000.408
납부매체비율0.0000.6030.6390.1450.7640.4081.000
2023-12-12T12:15:02.195425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명납부매체전자고지여부납부연도납부매체
세목명1.0000.0260.0000.054
납부매체전자고지여부0.0261.0000.0000.927
납부연도0.0000.0001.0000.000
납부매체0.0540.9270.0001.000
2023-12-12T12:15:02.324536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부건수납부금액납부매체비율납부연도세목명납부매체납부매체전자고지여부
납부건수1.0000.8820.8690.0000.1120.1950.158
납부금액0.8821.0000.7400.0200.1790.1390.151
납부매체비율0.8690.7401.0000.0000.3000.2440.110
납부연도0.0000.0200.0001.0000.0000.0000.000
세목명0.1120.1790.3000.0001.0000.0540.026
납부매체0.1950.1390.2440.0000.0541.0000.927
납부매체전자고지여부0.1580.1510.1100.0000.0260.9271.000

Missing values

2023-12-12T12:14:58.305704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:14:58.538165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

시도명시군구명자치단체코드납부연도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
0대전광역시중구301402017등록면허세ARSN51436800.62
1대전광역시중구301402017등록면허세ARSY1278100.12
2대전광역시중구301402017자동차세ARSN4136896182051.43
3대전광역시중구301402017자동차세ARSY71699600.87
4대전광역시중구301402017재산세ARSN2444664856030.39
5대전광역시중구301402017재산세ARSY1877500.12
6대전광역시중구301402017주민세ARSN110181472013.7
7대전광역시중구301402017주민세ARSY132297301.62
8대전광역시중구301402017지방소득세ARSN61622000.75
9대전광역시중구301402017지방소득세ARSY2311200.25
시도명시군구명자치단체코드납부연도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
403대전광역시중구301402021종합토지세지자체방문N4673200.02
404대전광역시중구301402021주민세지자체방문N26687392809012.63
405대전광역시중구301402021지방소득세지자체방문N7432219146403.52
406대전광역시중구301402021지역자원시설세지자체방문N171009900.08
407대전광역시중구301402021취득세지자체방문N207349776580109.81
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412대전광역시중구301402021지방소득세페이사납부Y1510388600.2